Social Dimensions and Processes in Second Language Acquisition: Multilingual Socialization in Transnational Contexts
Bibliographic record
Abstract
Abstract Social aspects of second language acquisition (SLA) and the contexts in which people attempt to learn and use languages and seek to become integrated within new and changing cultures have been examined for decades from various theoretical perspectives. In this article, I present some of the ways in which ‘social’ experience is being theorized in SLA and in broader fields that intersect with SLA, such as linguistic anthropology. I then discuss how the Douglas Fir Group (DFG, 2016) originally portrayed the many interlinking factors affecting SLA in our multilingual world on several analytic levels and suggest ways of perhaps reconceptualizing the model while retaining its powerful heuristic value. Next, I describe language socialization research as 1 productive social approach and provide examples of research in 2 transnational domains—study abroad and heritage language learning—that demonstrate a multiscalar approach to examining social dimensions of language development and use. The article ends with a discussion of transdisciplinarity in SLA research. I suggest possibilities for team‐based research projects that aim to understand cases from multiple, integrated perspectives on different scales of analysis, and then provide a brief reflection on some of the troubling political ideologies that SLA researchers who embrace multilingualism must now confront on a daily basis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".